• Nuclear Engineering and Technology
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• v.44 no.7
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• pp.791-798
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• 2012

Since standardized fracture test specimens cannot be easily extracted from in-service components, several alternative fracture toughness test methods have been proposed to characterize the deformation and fracture resistance of materials. One of the more promising alternatives is the local approach employing the SP(Small Punch) testing technique. However, this process has several limitations such as a lack of anisotropic yield potential and tediousness in the damage parameter calibration process. The present paper investigates estimation of ductile fracture resistance(J-R) curve by FE(Finite Element) analyses using an anisotropic damage model and enhanced calibration procedure. In this context, specific tensile tests to quantify plastic strain ratios were carried out and SP test data were obtained from the previous research. Also, damage parameters constituting the Gurson-Tvergaard-Needleman model in conjunction with Hill's 48 yield criterion were calibrated for a typical nuclear reactor material through a genetic algorithm. Finally, the J-R curve of a standard compact tension specimen was predicted by further detailed FE analyses employing the calibrated damage parameters. It showed a lower fracture resistance of the specimen material than that based on the isotropic yield criterion. Therefore, a more realistic J-R curve of a reactor material can be obtained effectively from the proposed methodology by taking into account a reduced load-carrying capacity due to anisotropy.

• Computers and Concrete
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• v.25 no.1
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• pp.67-73
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• 2020

Traditionally used analytical approach to predict the fatigue failure of reinforced concrete (RC) structure is generally conservative and has certain limitations. The nonlinear finite element method (FEM) offers less expensive solution for fatigue analysis with sufficient accuracy. However, the conventional implicit dynamic analysis is very expensive for high level computation. Whereas, an explicit dynamic analysis approach offers a computationally operative modelling to predict true responses of a structural element under periodic loading and might be perfectly matched to accomplish long life fatigue computations. Hence, this study simulates the fatigue behaviour of RC beams with finite element (FE) assemblage presenting a simplified explicit dynamic numerical solution to show computer aided fatigue behaviour of RC beam. A commercial FEM package, ABAQUS has been chosen for this complex modelling. The concrete has been modelled as a 8-node solid element providing competent compression hardening and tension stiffening. The steel reinforcements are simulated as two-node truss elements comprising elasto-plastic stress-strain behaviour. All the possible nonlinearities are duly incorporated. Time domain analysis has been adopted through an automatic Newmark-β time incremental technique. The program consists of twelve RC beams to visualize the real behaviour during fatigue process and to obtain the reliability of the study. Both the numerical and experimental results indicate a redistribution of stresses along the time and damage accumulation of beam which severely affect the serviceability and ultimate capacity of RC beam. The output of the FEM analysis demonstrates good match with the experimental consequences which affirm the efficacy of the computer aided model. The controlled fatigue damage evolution at service fatigue load limits makes the FE model an efficient tool in predicting high cycle fatigue behaviour of RC structures.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.259-271
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• 2019

It is important to obtain reasonable predictions of the extent of the damage during maritime accidents such as ship collisions and groundings. Many fracture models based on different mechanical backgrounds have been proposed and can be used to estimate the extent of damage involving ductile fracture. The goal of this study was to compare the damage extents provided by some selected fracture models. Instead of performing a new series of material constant calibration tests, the fracture test results for the ship building steel EH36 obtained by Park et al. (2019) were used which included specimens with different geometries such as central hole, pure shear, and notched tensile specimens. The test results were compared with seven ductile fracture surfaces: Johnson-Cook, Cockcroft-Latham-Oh, Bai-Wierzbicki, Modified Mohr-Coulomb, Lou-Huh, Maximum shear stress, and Hosford-Coulomb. The linear damage accumulation law was applied to consider the effect of the loading path on each fracture surface. The Swift-Voce combined constitutive model was used to accurately define the flow stress in a large strain region. The reliability of these simulations was verified by the good agreement between the axial tension force elongation relations captured from the tests and simulations without fracture assignment. The material constants corresponding to each fracture surface were calibrated using an optimization technique with the minimized object function of the residual sum of errors between the simulated and predicted stress triaxiality and load angle parameter values to fracture initiation. The reliabilities of the calibrated material constants of B-W, MMC, L-H, and HC were the best, whereas there was a high residual sum of errors in the case of the MMS, C-L-O, and J-C models. The most accurate fracture predictions for the fracture specimens were made by the B-W, MMC, L-H, and HC models.

• Smart Structures and Systems
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• v.29 no.1
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.